--- title: CutMixUp keywords: fastai sidebar: home_sidebar summary: "fast.ai callbacks to add a combination of CutMix & MixUp, or CutMix, MixUp, & Augmentation." description: "fast.ai callbacks to add a combination of CutMix & MixUp, or CutMix, MixUp, & Augmentation." nb_path: "nbs/callback.cutmixup.ipynb" ---
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class MixUp[source]

MixUp(alpha:float=0.4, interp_label:bool | None=None) :: MixHandlerX

Implementation of https://arxiv.org/abs/1710.09412. Supports MultiLoss

Type Default Details
alpha float 0.4 Alpha & beta parametrization for Beta distribution
interp_label bool or None None Blend or stack labels. Defaults to loss' y_int if None
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class CutMix[source]

CutMix(alpha:float=1.0, uniform:bool=True, interp_label:bool | None=None) :: MixHandlerX

Implementation of https://arxiv.org/abs/1905.04899. Supports MultiLoss

Type Default Details
alpha float 1.0 Alpha & beta parametrization for Beta distribution
uniform bool True Uniform patches across batch. True matches fastai CutMix
interp_label bool or None None Blend or stack labels. Defaults to loss' y_int if None
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class CutMixUp[source]

CutMixUp(mix_alpha:float=0.4, cut_alpha:float=1.0, mixup_ratio:Number=1, cutmix_ratio:Number=1, cutmix_uniform:bool=True, element:bool=True, interp_label:bool | None=None) :: MixUp

Combo implementation of https://arxiv.org/abs/1710.09412 and https://arxiv.org/abs/1905.04899"

Supports element-wise application of MixUp and CutMix on a batch.

Type Default Details
mix_alpha float 0.4 MixUp alpha & beta parametrization for Beta distribution
cut_alpha float 1.0 CutMix alpha & beta parametrization for Beta distribution
mixup_ratio Number 1 Ratio to apply MixUp relative to CutMix
cutmix_ratio Number 1 Ratio to apply CutMix relative to MixUp
cutmix_uniform bool True Uniform patches across batch. True matches fastai CutMix
element bool True Apply element-wise MixUp and CutMix on a batch
interp_label bool or None None Blend or stack labels. Defaults to loss' y_int if None
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class CutMixUpAugment[source]

CutMixUpAugment(mix_alpha:float=0.4, cut_alpha:float=1.0, mixup_ratio:Number=1, cutmix_ratio:Number=1, augment_ratio:Number=1, augment_finetune:Number | None=None, cutmix_uniform:bool=True, cutmixup_augs:listified[Transform | Callable[..., Transform]] | None=None, element:bool=True, interp_label:bool | None=None) :: MixUp

Combo implementation of https://arxiv.org/abs/1710.09412 and https://arxiv.org/abs/1905.04899 plus Augmentation.

Supports element-wise application of MixUp, CutMix, and Augmentation on a batch.

Pulls augmentations from Dataloaders.train.after_batch. These augmentations are not applied when performing MixUp & CutMix, the frequency is controlled by augment_ratio.

Use augment_finetune to only apply dataloader augmentations at the end of training.

cutmixup_augs are an optional separate set of augmentations to apply with MixUp and CutMix. Usually less intensive then the dataloader augmentations.

Type Default Details
mix_alpha float 0.4 MixUp alpha & beta parametrization for Beta distribution
cut_alpha float 1.0 CutMix alpha & beta parametrization for Beta distribution
mixup_ratio Number 1 Ratio to apply MixUp relative to CutMix & augmentations
cutmix_ratio Number 1 Ratio to apply CutMix relative to MixUp & augmentations
augment_ratio Number 1 Ratio to apply augmentations relative to MixUp & CutMix
augment_finetune Number or None None Number of epochs or pct of training to only apply augmentations
cutmix_uniform bool True Uniform patches across batch. True matches fastai CutMix
cutmixup_augs listified[Transform or Callable[..., Transform]] or None None Augmentations to apply before MixUp & CutMix. Should not have Normalize
element bool True Apply element-wise MixUp, CutMix, and Augment on a batch
interp_label bool or None None Blend or stack labels. Defaults to loss' y_int if None
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CutMixUp Examples

CutMix with uniform cuts

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with less_random():
    cutmix = CutMixUp(cutmix_ratio=1, mixup_ratio=0, element=False)
    test_cutmixup(cutmix)
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CutMix with random cuts

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with less_random():
    cutmix = CutMixUp(cutmix_ratio=1, mixup_ratio=0, cutmix_uniform=False, element=False)
    test_cutmixup(cutmix)
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MixUp

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with less_random():
    mixup = CutMixUp(mix_alpha=1., cutmix_ratio=0, mixup_ratio=1, element=False)
    test_cutmixup(mixup)
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CutMix and MixUp on the same batch

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with less_random():
    mixup = CutMixUp(mix_alpha=1., cutmix_ratio=1, mixup_ratio=1)
    test_cutmixup(mixup)
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CutMixAugment Examples

CutMix with weak augmentations

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with less_random():
    cutmix = CutMixUpAugment(cutmix_ratio=1, mixup_ratio=0, augment_ratio=0, cutmix_uniform=False,
                             cutmixup_augs=aug_transforms(size=112, max_warp=0.1, max_lighting=0.1),
                             element=False)
    test_cutmixup(cutmix, True, size=112)
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MixUp with weak augmentations

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with less_random():
    mixup = CutMixUpAugment(mix_alpha=1., cutmix_ratio=0, mixup_ratio=1, augment_ratio=0,
                            cutmixup_augs=aug_transforms(max_rotate=20),
                            element=False)
    test_cutmixup(mixup, True)
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Just the strong Augmentations

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with less_random():
    augment = CutMixUpAugment(cutmix_ratio=0, mixup_ratio=0, augment_ratio=1, element=False)
    test_cutmixup(augment, True)
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Mixup, CutMix, and strong Augmentations on the same batch

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with less_random():
    batch = CutMixUpAugment(mix_alpha=1., cutmix_ratio=1, mixup_ratio=1, augment_ratio=1, cutmix_uniform=False)
    test_cutmixup(batch, True)
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Mixup and CutMix with weak augmentations and strong Augmentations on the same batch

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with less_random():
    batch = CutMixUpAugment(mix_alpha=1., cutmix_ratio=1, mixup_ratio=1, augment_ratio=1,
                            cutmix_uniform=False, cutmixup_augs=aug_transforms(max_rotate=20))
    test_cutmixup(batch, True)
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